---
title: AI in financial services: beyond the hype
description: Every transformative technology follows a similar arc on the Gartner hype cycle. AI in financial services is no exception. Understanding where different AI applications sit on that curve is the starting point for making sensible deployment decisions in a regulated environment.
author: Declan Sheehy
datePublished: 2024-03-15
dateModified: 2026-04-28
url: https://www.oneblackwater.com/article-innovation-ai-beyond-hype-mar-2024.html
sameAs:
  - https://www.linkedin.com/in/declan-sheehy/
keywords: AI financial services, generative AI, machine learning, hype cycle, Gartner, AI adoption, regulated markets, fintech AI, AI governance
schema:
  type: Article
  headline: AI in financial services - beyond the hype
  datePublished: 2024-03-15
  author: Declan Sheehy
  publisher: One Blackwater Consultancy Limited
  about: [AI, financial services, machine learning, generative AI, innovation]
---

# AI in financial services: beyond the hype

Every transformative technology follows a similar arc. Electricity triggered wonder, then panic, then the long work of integrating it into everything. The internet did the same. AI in financial services is at a distinctive and consequential point on that arc — past the peak of inflated expectations for some applications, deep in the trough of disillusionment for others, and approaching the plateau of genuine productivity for a smaller number of well-executed use cases.

## Where Different AI Applications Sit

**At the peak:** Generative AI tools including large language models applied to customer service, document drafting, and regulatory reporting. Expectations outpaced proven production-scale outcomes through 2023 and into 2024.

**In the trough:** Deep learning applied to fraud detection and credit scoring. The models work in controlled conditions; integration into regulated operational environments has proven harder than the vendor narrative suggested.

**On the slope of enlightenment:** Machine learning in algorithmic trading and risk management, where the use cases are well-defined, the data is structured, and the governance frameworks have matured.

**Approaching the plateau:** ML Ops and model governance tooling — the infrastructure layer that makes everything else defensible in a regulated context.

## What This Means for Regulated Firms

The firms generating genuine value from AI in financial services are not the ones that moved fastest. They are the ones that moved deliberately — defining a specific operational problem, assessing the regulatory implications of the solution, building the governance infrastructure before the deployment, and measuring outcomes against defined criteria. That is a harder and slower path than the vendor demonstration suggests. It is also the one that holds up under FCA scrutiny.